


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  inspect.getfile(accelerate.test_utils    )
        __a					=  os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_script.py"""]    )
        __a					=  os.path.sep.join(
            mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]    )
        __a					=  os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_ops.py"""]    )


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        print(f'''Found {torch.cuda.device_count()} devices.'''    )
        __a					=  ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(__A  ,       env=os.environ.copy()    )


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        print(f'''Found {torch.cuda.device_count()} devices.'''    )
        __a					=  ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
        print(f'''Command: {cmd}'''    )
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(__A  ,       env=os.environ.copy()    )


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
        with patch_environment(omp_num_threads=1    ):
           execute_subprocess_async(__A  ,       env=os.environ.copy()    )


     @require_multi_gpu
     def 	snake_case_  (	self    ):
        print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''    )
        __a					=  ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
        with patch_environment(omp_num_threads=1  ,       cuda_visible_devices="""0,1"""    ):
           execute_subprocess_async(__A  ,       env=os.environ.copy()    )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							Accelerator()
  SCREAMING_SNAKE_CASE							    =							(accelerator.state.process_index + 2, 1_0)
  SCREAMING_SNAKE_CASE							    =							torch.randint(0, 1_0, shape).to(accelerator.device)

  SCREAMING_SNAKE_CASE							    =							''

  SCREAMING_SNAKE_CASE							    =							accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  SCREAMING_SNAKE_CASE							    =							accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  SCREAMING_SNAKE_CASE							    =							accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)





from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Optional[int]						=    len(lowerCamelCase    )
				# We need to create solution object to save path.
				UpperCamelCase_:				List[str]						=    [[0 for _ in range(lowerCamelCase    )] for _ in range(lowerCamelCase    )]
				UpperCamelCase_:				Dict						=    run_maze(lowerCamelCase  ,     0  ,     0  ,     lowerCamelCase    )
				if solved:
								print("""\n""".join(str(lowerCamelCase    ) for row in solutions    )    )
				else:
								print("""No solution exists!"""    )
				return solved







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Dict						=    len(lowerCamelCase    )
				# Final check point.
				if i == j == (size - 1):
								UpperCamelCase_:				Union[str, Any]						=    1
								return True

				UpperCamelCase_:				List[str]						=    (not i < 0) and (not j < 0)  # Check lower bounds
				UpperCamelCase_:				List[str]						=    (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								UpperCamelCase_:				Dict						=    (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												UpperCamelCase_:				Optional[Any]						=    1

												# check for directions
												if (
												    run_maze(lowerCamelCase  ,     i + 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j + 1  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     i - 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j - 1  ,     lowerCamelCase    )
												):
																return True

												UpperCamelCase_:				Optional[Any]						=    0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()






